Stephen Friend Institute of Development, Aging and Cancer 2011-11-29

Issues with Current Drug Discovery

A Proposal

Arch2POCM
A Drug Development Approach
from Disease Targets to their Clinical Validation

Stephen Friend
Sage Bionetworks
(a non-profit foundation)
Sendai November 2011

Alzheimers Diabetes

Treating Symptoms v.s. Modifying Diseases
Depression Cancer
Will it work for me?

Extensive Publications now Substantiating Scientific Approach
Probabilistic Causal Bionetwork Models
• >80 Publications from Rosetta Genetics

Metabolic "Genetics of gene expression surveyed in maize, mouse and man." Nature. (2003)
Disease "Variations in DNA elucidate molecular networks that cause disease." Nature. (2008)
"Genetics of gene expression and its effect on disease." Nature. (2008)
"Validation of candidate causal genes for obesity that affect..." Nat Genet. (2009)
….. Plus 10 additional papers in Genome Research, PLoS Genetics, PLoS Comp.Biology, etc
CVD "Identification of pathways for atherosclerosis." Circ Res. (2007)
"Mapping the genetic architecture of gene expression in human liver." PLoS Biol. (2008)
…… Plus 5 additional papers in Genome Res., Genomics, Mamm.Genome

Bone "Integrating genotypic and expression data …for bone traits…" Nat Genet. (2005)
d
..approach to identify candidate genes regulating BMD…" J Bone Miner Res. (2009)
Methods "An integrative genomics approach to infer causal associations ... Nat Genet. (2005)
"Increasing the power to detect causal associations… PLoS Comput Biol. (2007)
"Integrating large-scale functional genomic data ..." Nat Genet. (2008)
…… Plus 3 additional papers in PLoS Genet., BMC Genet.

List of Influential Papers in Network Modeling

  50 network papers
  http://sagebase.org/research/resources.php

Requires Data driven Science

Lots of data, tools, evolving models of disease

Requires Scientists Clinicians & Citizens to link in different ways

Sage Mission
Sage Bionetworks is a non-profit organization with a vision to
create a commons where integrative bionetworks are evolved by
contributor scientists with a shared vision to accelerate the
elimination of human disease

Building Disease Maps Data Repository

Commons Pilots Discovery Platform
Sagebase.org

Sage Bionetworks Collaborators

  Pharma Partners
  Merck, Pfizer, Takeda, Astra Zeneca, Amgen

  Foundations
  CHDI, Gates Foundation

  Government
  NIH, LSDF

  Academic
  Levy (Framingham)
  Rosengren (Lund)
  Krauss (CHORI)

  Federation
  Ideker, Califarno, Butte, Schadt 10

Engaging Communities of Interest
NEW MAPS
Disease Map and Tool Users-
( Scientists, Industry, Foundations, Regulators...)

PLATFORM
Sage Platform and Infrastructure Builders-
( Academic Biotech and Industry IT Partners...)

RULES AND GOVERNANCE
Data Sharing Barrier Breakers-
(Patients Advocates, Governance
and Policy Makers, Funders...)
ORM
APS

NEW TOOLS
M

F
PLAT

Data Tool and Disease Map Generators-
NEW

(Global coherent data sets, Cytoscape,
Clinical Trialists, Industrial Trialists, CROs…)
RULES GOVERN
PILOTS= PROJECTS FOR COMMONS
Data Sharing Commons Pilots-
(Federation, CCSB, Inspire2Live....)
Arch2POCM

Bin Zhang
Model of Breast Cancer: Integration Xudong Dai
Jun Zhu
Conserved Super-modules

mRNA proc.
= predictive
Breast Cancer Bayesian Network

Chromatin
of survival

Extract gene:gene relationships for selected super-modules from BN and define Key Drivers

Pathways & Regulators
(Key drivers=yellow; key drivers validated in siRNA screen=green)
Cell Cycle (Blue) Chromatin Modification (Black) Pre-mRNA proc. (Brown) mRNA proc. (red)

Zhang B et al., Key Driver Analysis in Gene Networks (manuscript)

Section 1 – Project Overview

Non-Responder Cancer Project Mission

To identify Non-Responders to approved drug regimens in
order to improve outcomes, spare patients unnecessary
toxicities from treatments that have no benefit to them, and
reduce healthcare costs

Sage Bionetworks • Non-Responder Project


The Non-Responder Project is an international initiative with funding for 6 initial
cancers anticipated from both the public and private sectors

GEOGRAPHY United States China

TARGET
CANCER
Ovarian Renal Breast AML Colon Lung

FUNDING Likely to be
SOURCE funded by the Pilot Funded by the Chinese private
Seeking private sector funding
Federal sector partners
Government



The Non-Responder Cancer Project Leadership Team

Stephen Friend, MD, PhD Todd Golub, MD
President and Co-Founder of Sage Founding Director Cancer Biology
Bionetworks, Head of Merck Oncology Program Broad Institute, Charles
01-08, Founder of Rosetta Dana Investigator Dana-Farber
Inpharmatics 97-01, co-Founder of the Cancer Institute, Professor of
Seattle Project Pediatrics Harvard Medical School,
Investigator, Howard Hughes Medical
Institute

“This study aims to provide both a material near term “Having focused on molecular medicine in my
improvement in cancer patient outcomes and a long term decades of conducting clinical trials, I am excited by
blueprint for the future of oncology trails, prognosis and the opportunity for the Non-responder project to
care. I believe the team of scientific, clinical and patient change the way we select treatments for patients. My
advocate partners we have assembled is unique in its passion for this project and for improving our ability to
ability to execute this study. With public and private better target therapies is immeasurable and I look
sector support, I know we will be able to change the forward to being an active part of this research.”
future of cancer care and research around the world.”



The Non-Responder Cancer Project Leadership Team

Charles Sawyers, MD Richard Schilsky, MD
Chair, Human Oncology Memorial Chief, Hematology- Oncology, Deputy
Sloan-Kettering Cancer Center, Director, Comprehensive Cancer
Investigator, Howard Hughes Medical Center, University of Chicago; Chair,
Institute, Member, National Academy National Cancer Institute Board of
of Sciences, past President American Scientific Advisors; past-President
Society of Clinical Investigation, 2009 ASCO, past Chairman CALGB clinical
Lasker-DeBakey Clinical Medical trials group
Research Award

“I have considered many opportunities to engage in “Stephen and I have worked together for many years on
personalized medicine, and believe the greatest value can developing innovative network approaches to analyzing
be in developing assays to better target treatments for disease. Identifying signatures of non-response is the most
patients at the molecular level. I have worked with Stephen exciting project I have been involved with in recent years
for 3 years and believe he is uniquely qualified to lead a and one which I believe can dramatically shift the way
project of this caliber to great success.” cancer patients receive treatment.”


Section 2 – Research Plan

For each tumor-type, the non-responder project will follow a common workflow, with patient
identification and sample collection the most variable across studies

Non-Responder Project Workflow

Identification and enrollment, and data and sample The remaining parts of the study will be largely similar, and
collection may differ by tumor-type potentially shared, across all projects

Data
and
Clinical

Iden%ﬁca%on
and
Sample
Disease
Feedback

Sample
Data

Enrollment
Processing
Modeling
and
Results

Collec%on
Repor%ng

Payment and Reimbursement

Project Management



Identification and Enrollment
The number of patients and enrollment procedures will vary for each study based on the biology
and stage of the disease and the size of the advocate community

•  The number of patients differs according to the biology of each
tumor-type being investigated
Ovarian Cancer
How many patients •  The sample will require enough patients to identify 100-150 patients
are required? In Ovarian Cancer, the target patient population will be those who experience
for each arm (responders and non-responders) that have distinct
biology
recurrence within 6-24 months of stopping initial treatment. This population
will require enrollment of 150 patients to identify groups with distinct
response/non-response biology
•  Enrollment sources will vary based on the makeup of the physician
Ovarian Cancer Patients
Who will be and patient communities
responsible for •  Each study will entail a mix of physician-driven and patient-initiated
enrollment , with those with strong advocate communities trending
enrolling patients? + Initial Response* Surgical removal No initial response*
towards patient-initiated, and those with leverageable physician 80% and initial chemo 20%

relationships involving more physician targeting

No recurrence Recurrence Second series of
<24mo 6-24 months Doublet Chemo
•  Data will include a questionnaire to determine eligibility and to
What data will need collect additional information that may inform analysis (e.g. age,
to be collected at race, etc.) Responders Non-Responders
•  Additionally, patient consent will need to be obtained 30-50% 50-70%
enrollment?
•  Genetic Alliance will own and standardize the consenting process

Since most ovarian cancer patients see a Gynecologic 30% Patient-
Oncologist who manages the entirety of their treatment, initiated
•  Costs to identify and enroll patients will vary by channel
What will be the this tumor-type is well structured to use a select group of
•  Patient-driven will be predominantly marketing and shipping costs
cost of (e.g. marketing through the Love/Army of women costs $1500 until physicians/AMCs to target patients for enrollment 70% Physician-
identification and study is filled) driven
enrollment? •  Physician-driven enrollment may require educating physicians and a
grant of approximately $20,000 per patient plus some administrative
expenses



Sample Processing
Sample processing will involve whole genome sequencing, conducted at leading TCGA
participating sequencing centers, as well as bioinformatics and pathological review

Labs
&
Pathology
Gene%c
Analysis
Core
Bioinforma%cs

•  Each
cancer
type
will
•  Analysis
will
include:
•  Bioinforma%cs
will
be

have
designated
sites
Whole
Genome
conducted
by
the
most

for
conduc%ng
rou%ne
Sequencing,
cost-‐eﬀec%ve,
trusted

labs
and
pathological
transcriptome
gene
provider
to
ensure
the

review
to

ensure
expression
and
copy
quality
and
consistency

consistency
of
analysis
number
varia%on
of
data
for
analysis

•  Each
study
will
have
a
•  The
core

primary
processing
bioinforma%cs

site,
which
will
be
processing
will
turn
the

selected
from
among
raw
data
into
usable

leaders
in
gene%c
altera%on
component

sequencing
that
have
lists
of
muta%ons
and

par%cipated
in
similar
dele%ons

projects,
such
as
The

Cancer
Genome
Atlas



Data Collating and Disease Modeling
The genetic and clinical information will be combined and analyzed by Sage Bionetworks to
design a disease model identifying the causes of non-response
1 2 3
Combines genomic and Applies sophisticated Generates a map of drivers
clinical data mathematical modeling of non-response

All scientific output will be publicly available and
no members of the research group will own any
resulting IP


Arch2POCM

A
Fundamental
Systems
Change

for
Drug
Discovery

Stephen
Friend
Aled
Edwards
Chas
Bountra

Lex
vander
Ploeg,
Thea
Norman,
Keith
Yamamoto

“Absurdity”
of
Current
R&D
Ecosystem

•  $200B
per
year
in
biomedical
and
drug
discovery
R&D

•  Handful
of
new
medicines
approved
each
year

•  Produc%vity
in
steady
decline
since
1950

•  90%
of
novel
drugs
entering
clinical
trials
fail

•  NIH
and
EU
just
started
spending
billions
to
duplicate
process

•  98%
of
pharma
revenues
from
compounds
approved
more

than
5
years
ago
(average
patent
life
11
years)

•  >50,000
pharma
employees
ﬁred
in
each
of
last
three
years

•  Number
of
R&D
sites
in
Europe
down
from
29
to
16
in
2009

What
is
the
problem?

•  Regulatory
hurdles
too
high?

•  Low
hanging
fruit
picked?

•  Payers
unwilling
to
pay?

•  Genome
has
not
delivered?

•  Valley
of
death?

•  Companies
not
large
enough
to
execute
on
strategy?

•  Internal
research
costs
too
high?

•  Clinical
trials
in
developed
countries
too
expensive?

•  In
fact,
all
are
true
but
none
is
the
real
problem

What
is
the
problem?

• 

The
current
system
is
designed
as
if
every
new

program
is
des%ned
to
deliver
an
approved
drug

•  Each
new
therapy
is
pursued
through
use
of

proprietary
compounds
moving
in
parallel
with
no

data
being
shared
(ohen
5-‐10
companies
at
a
%me)

•  Therefore
it
makes
complete
sense
to
maintain

secrecy

•  But
we
have
no
clue
what
we’re
doing

• 
Alzheimer’s,
cancer,
schizophrenia,
au%sm.….

The current pharma model is redundant

Target ID/ Hit/Probe/ Clinical Toxicolog Phase I
Phase
Discovery Lead ID Candidate y/ IIa/IIb
Phase
Target ID/ Hit/Probe/ Clinical
ID Pharmaco
Toxicolog Phase I
Discovery Lead ID Candidate logy
y/ IIa/IIb
ID Pharmaco
logy
Toxicolog Phase I
Phase
ID Pharmaco
Target ID/ Hit/Probe/ Clinical Toxicolog
logy Phase I
Phase
ID Pharmaco
logy
Target ID/ Hit/Probe/ Clinical Toxicolog Phase I
Phase
ID Pharmaco
logy
Toxicolog Phase I
Phase
ID Pharmaco
Target ID/ Hit/Probe/ Clinical Toxicolog
logy Phase I
Phase
ID Pharmaco
logy

50% 10% 30% 30% 90%
Attrition

Negative POC information is not shared

What
is
the
problem?

The
real
problem
is
that
the
current
system
is
unable

to
provide
the
needed
insights
into
human
biology
and

disease
required

We
need
to
develop
a
mechanism
to
be8er

understand
disease
biology
before
tes:ng
compe::ve

compounds
on
sick
people

The Solution: Arch2POCM

A globally distributed public private partnership (PPP) committed to:

• Generate more clinically validated targets by sharing data

• Help deliver more new drugs for patients

27
27

Arch2POCM: What Will It Do?
•  Arch2POCM will focus on targets that are deemed too risky for either public
or private sectors in the disease areas of cancer/immunology and
shizophrenia/autism

•  Arch2POCM will devleop and use test compounds to de-risk these targets
and determine if the targets play a role in the biology of human disease

•  Arch2POCM will share the risk by pooling public and private resources

•  Arch2POCM will file no patents and place all data into the public domain:
•  Enables the pharmaceutical sector to use this information to start, refine or shut down their
own proprietary efforts
•  Crowdsourcing expands our understanding of human biology and the number of ideas that
can be pursued without additional funds

•  Arch2POCM will make the test compounds available to academic groups and
foundations so they can use them to explore a multitude of additional
indications
28

Why Data Sharing Through To Phase IIb?

•  Most rapid approach to reveal limitations and
opportunities associated with the target

•  Increases probability of success for internal proprietary
programs

•  Scientific decisions are not influenced by market
considerations or biased internal thinking

•  Target mechanism is only properly tested at Phase IIb

29

Why No IP on “Common Stream” Compounds?
•  Allows multiple groups to test compounds in diverse
indications without funds from Arch2POCM- crowdsourcing
drug discovery

•  Broader and faster data dissemination

•  Far fewer legal agreements to negotiate

•  Generates “freedom to operate” on target because there are
no patent thickets to wade through

•  Efficient way to access world’s top scientists and doctors
without hassle

30

The Benefits of the Arch2POCM Pre-competitive
Model: Crowdsourced Studies

The
Crowd

Arch2POCM
Compounds
And Data

Crowdsourced data on
Arch2POCM test compound
• SAR
med
chem

• Best
indica%on

Clin

• Clinical
data

Crowdsourced
studies
on
Arch2POCM
test
compounds
will
provide
clinical
informa:on

about
the
pioneer
targets
in
MANY
indica:ons

31

Arch2POCM: Scale and Scope
•  Proposed Goal: Initiate 2 programs. One for Epigenetic Oncology/
Immunology. One for Neuroscience/Schizophrenia/Autism. Both
programs will have 8 drug discovery projects (targets) - ramped up
over a period of 2 years

•  These will be executed over a period of 5 years making a total of 16
drug discovery projects

•  We project a five-year budget of $200-250M in order to advance up
to 8 drug discovery projects within each of the two therapeutic
programs.

•  Arch2POCM funding will come from a combination of public funding
from governments (50%) and private sector funding from
pharmaceutical and biotechnology companies (25%) and from
private philanthropists (25%)

32

Epigenetics/ Chromatin Biology:
Arch2POCM s Selected Pioneer Area of Oncology/
Immunology Drug Discovery

33

How We Define Epigenetics

Lysine

DNA

Histone

Modification Write Read Erase

Acetyl HAT Bromo HDAC
Methyl HMT MBT DeMethyl
34

The Case for Epigenetics/Chromatin Biology

1.  There are epigenetic oncology drugs on the market (HDACs)

2.  A growing number of links to oncology, notably many genetic links (i.e.
fusion proteins, somatic mutations)

3.  A pioneer area: More than 400 targets amenable to small molecule
intervention - most of which are only recently shown to be “druggable”,
and only a few of which are under active investigation

4.  Open access, early-stage science is developing quickly – significant
collaborative efforts (e.g. SGC, NIH) to generate proteins, structures,
assays and chemical starting points

35

Examples of Epigenetic Links to Cancer
•  Ezh2 methyltransferase (enhancer of zeste homolog 2)
–  Somatic mutations in B-cell lymphoma
•  JARID1B demethylase (jumonji, AT rich interactive domain 2 )
–  Linked to malignant transformation: expressed at high levels in breast and prostate
cancers; Knock-down inhibits proliferation of breast cancer lines and tumor growth
•  G9A methyltransferase (euchromatic histone-lysine N-methyltransferase
2)
–  Expressed in aggressive lung cancer cells: high expression correlates to poor
prognosis; G9a knockdown inhibits metastasis in vivo
•  MLL: myeloid/lymphoid or mixed-lineage leukemia
–  Multiple chromosomal translocations involving this gene are the cause of certain acute
lymphoid leukemias and acute myeloid leukemias
•  Brd4: (Bromodomain-containing protein 4)
–  Implicated in t(15; 19) aggressive carcinoma: Chromosome 19 translocation
breakpoint interrupts the coding sequence of a bromodomain gene, BRD4
•  CBP bromodomain
–  Oncogeneic fusions
–  Mutated in relapsing AML
36

The Current Epigenetics Universe (2011)
Domain Family Typical substrate class* Total
Targets
Histone Lysine Histone/Protein K/R(me)n/ (meCpG) 30

demethylase
Bromodomain Histone/Protein K(ac) 57

R Tudor domain Histone Kme2/3 - Rme2s 59

O
Chromodomain Histone/Protein K(me)3 34

Y
A MBT repeat Histone K(me)3 9

L
PHD finger Histone K(me)n 97

Acetyltransferase Histone/Protein K 17

Methyltransferase Histone/Protein K&R 60

PARP/ADPRT Histone/Protein R&E 17

MACRO Histone/Protein (p)-ADPribose 15

Histone deacetylases Histone/Protein KAc 11

395

Now known to be amenable to small molecule inhibition 37

Some Potential Arch2POCM Epigenetic
Oncology/Immunology Targets

38

Open
Access
Test
Compounds
and
Tools
Are
Available
For

Arch2POCM
Teams

Probe
Kd < 100 nM G9a/GLP
Selectivity > 30x
Criteria
Cell IC50 < 1 µM BET

SETD7 JMJD2
Kd < 500 nM PHF8 FBXL11
Selectivity > 30x
BET 2nd
Screening / Chemistry

SUV39H2
Active
L3MBTL3 GCN5L2
EP300
Kd < 5 µM L3MBTL1 JMJD3
WDR5

HAT1 PRMT3
BRD
Weak CREBBP BAZ2B PB1
KDM
FALZ SETD8
HAT
MYST3 EZH2
JARID1C Me Lys Binders
None DOT1L JMJD2A Tu
SMYD3 UHRF1 HMT

In vitro assay  Cell assay 
available available
Assay Development
39

Proposed IT Infrastructure For Arch2POCM Data
Sharing
Arch2POCM
funded
Research Crowdsourced Research

Academic
Basic

Clinical
Research

Research

ac%vi%es

CRO
or

and
internal
use
requires
signiﬁcant

CRO

CRO

Data
base
design
for
crowdsourced

Independent
Independent

funded

investment
in
data
base
structure

preclinical
Clinical
Basic
Res
Clin
Res

Basic

IT
structure

Data
management

Good
QC
and

control
and
Harmonize
data
management
compliance

IT
data
with

base
reduced

structure

control

1.  Arch2POCM
QC
data
and
IT
structure

2.  Database
from
crowdsourcing
with
volunteer
contr.

3.  Published
data

40

General Benefits of Arch2POCM For Drug
Development
1.  Arch2POCM s use of test compounds to de-risk previously unexplored
biology enables drug developers to initiate proprietary drug
development with an array of unbiased, clinically validated targets
  Arch2POCM operates without patents and advances pairs of test
compounds through Ph II
  Test compounds are used by Arch2POCM and the crowd to define clinical
mechanisms for epigenetic targets impacting oncology
2.  Arch2POCM crowdsourced research and trials provides parallel shots
on goal: by aligning test compounds to most promising unmet medical
need

3.  Negative clinical trial information generated by Arch2POCM and the
crowd will increase clinical success rates (as one can pick targets and
indications more smartly)

4.  Build methods to track and visualize crowd sourced data, clinical safety
profiles and reporting of potential adverse event
41

Arch2POCM Value Propositions For Academia

•  Funding to pursue and publish disruptive discovery research

•  Sharing of resources and data among private and academic
partners

•  Development of basic discoveries toward therapies and cures

•  Collaboration with private sector and regulatory scientists

•  Education of students and public about nature and process of
discovery, and understanding disease

•  Exit options: extend/branch studies beyond arch2POCM

42

Example
6:
Sage
Congress
2012
Pa%ent
Project

Stephen Friend Institute of Development, Aging and Cancer 2011-11-29

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